add_torch_in_requirementstxt

app-memora.py

@@ -1,169 +0,0 @@
-from pathlib import Path
-
-import streamlit as st
-from googlesearch import search
-import pandas as pd
-import os
-from rag_sec.document_search_system import DocumentSearchSystem
-from chainguard.blockchain_logger import BlockchainLogger
-from PIL import Image
-from itertools import cycle
-
-# Blockchain Logger
-blockchain_logger = BlockchainLogger()
-
-# Directory for storing uploaded files
-UPLOAD_DIR = "uploaded_files"
-os.makedirs(UPLOAD_DIR, exist_ok=True)
-
-# Initialize DocumentSearchSystem
-@st.cache_resource
-def initialize_system():
-    """Initialize the DocumentSearchSystem and load documents."""
-    system = DocumentSearchSystem(
-        neo4j_uri="neo4j+s://0ca71b10.databases.neo4j.io",
-        neo4j_user="neo4j",
-        neo4j_password=""
-    )
-    system.retriever.load_documents()
-    return system
-
-# Initialize the system
-system = initialize_system()
-
-st.title("Memora: Secure File Upload and Search with Blockchain & Neo4j")
-st.subheader("Personalized news and global updates at your fingertips")
-# File Upload Section
-uploaded_files = st.file_uploader("Upload your files", accept_multiple_files=True, type=['jpg', 'jpeg', 'png', 'mp4', 'avi'])
-
-if uploaded_files:
-    for uploaded_file in uploaded_files:
-        # Save file locally
-        file_path = os.path.join(UPLOAD_DIR, uploaded_file.name)
-        with open(file_path, "wb") as f:
-            f.write(uploaded_file.getbuffer())
-        st.success(f"File saved locally: {file_path}")
-
-        # Display uploaded file details
-        if uploaded_file.type.startswith('image'):
-            image = Image.open(uploaded_file)
-            st.image(image, caption=uploaded_file.name, use_column_width=True)
-
-        # Metadata Input
-        album = st.text_input(f"Album for {uploaded_file.name}", "Default Album")
-        tags = st.text_input(f"Tags for {uploaded_file.name} (comma-separated)", "")
-
-        # Log Metadata and Transaction
-        if st.button(f"Log Metadata for {uploaded_file.name}"):
-            metadata = {"file_name": uploaded_file.name, "tags": tags.split(','), "album": album}
-            blockchain_details = blockchain_logger.log_data(metadata)
-            blockchain_hash = blockchain_details.get("block_hash", "N/A")
-
-            # Use Neo4jHandler from DocumentSearchSystem to log the transaction
-            system.neo4j_handler.log_relationships(uploaded_file.name, tags, blockchain_hash, [album])
-            st.write(f"Metadata logged successfully! Blockchain Details: {blockchain_details}")
-
-# Blockchain Integrity Validation
-if st.button("Validate Blockchain Integrity"):
-    is_valid = blockchain_logger.is_blockchain_valid()
-    st.write("Blockchain Integrity:", "Valid ✅" if is_valid else "Invalid ❌")
-
-# Document Search Section
-st.subheader("Search Documents")
-
-# Google Search: User-Specific News
-st.subheader("1. Latest News About You")
-user_name = st.text_input("Enter your name or handle to search for recent news", value="Talex Maxim")
-
-if st.button("Search News About Me"):
-    if user_name:
-        st.write(f"Searching Google for news about **{user_name}**...")
-        try:
-            results = list(search(user_name, num_results=5))
-            if results:
-                st.success(f"Top {len(results)} results for '{user_name}':")
-                user_news_data = {"URL": results}
-                df_user_news = pd.DataFrame(user_news_data)
-                st.dataframe(df_user_news)
-        except Exception as e:
-            st.error(f"An error occurred during the search: {str(e)}")
-    else:
-        st.warning("Please enter your name or handle to search.")
-
-# Google Search: Global News Categories
-categories = ["Technology", "Sports", "Politics", "Entertainment", "Science"]
-
-st.title("Global News Insights")
-
-# News Results Dictionary
-news_results = {}
-
-try:
-    # Fetch News for Each Category
-    for category in categories:
-        try:
-            news_results[category] = list(search(f"latest {category} news", num_results=3))
-        except Exception as e:
-            news_results[category] = [f"Error fetching news: {str(e)}"]
-
-    # Display Results with Styled Buttons
-    for category, articles in news_results.items():
-        st.subheader(f"{category} News")
-        cols = st.columns(3)  # Create 3 columns for the layout
-
-        if articles and "Error fetching news" not in articles[0]:
-            for idx, article in enumerate(articles):
-                with cols[idx % 3]:  # Cycle through columns
-                    st.markdown(
-                        f"""
-                        <div style="padding: 10px; border: 1px solid #ccc; border-radius: 5px; margin: 10px; text-align: center;">
-                            <a href="{article}" target="_blank" style="text-decoration: none;">
-                                <button style="background-color: #c4ccc8; color: white; border: none; padding: 10px 20px; text-align: center; display: inline-block; font-size: 16px; border-radius: 5px;">
-                                    {category}-{idx + 1}
-                                </button>
-                            </a>
-                        </div>
-                        """,
-                        unsafe_allow_html=True,
-                    )
-        else:
-            st.warning(f"Could not fetch news for **{category}**.")
-except Exception as e:
-    st.error(f"An unexpected error occurred: {str(e)}")
-
-
-    #     # Display results
-    #     for category, articles in news_results.items():
-    #         st.write(f"### Top News in {category}:")
-    #         for idx, article in enumerate(articles, start=1):
-    #             st.write(f"{idx}. [Read here]({article})")
-    # except Exception as e:
-    #     st.error(f"An error occurred while fetching global news: {str(e)}")
-
-# Document Search
-st.subheader("3. Search Documents")
-query = st.text_input("Enter your query (e.g., 'sports news', 'machine learning')")
-
-if st.button("Search Documents"):
-    if query:
-        result = system.process_query(query)
-        if result["status"] == "success":
-            st.success(f"Query processed successfully!")
-            st.write("### Query Response:")
-            st.write(result["response"])
-            st.write("### Retrieved Documents:")
-            for idx, doc in enumerate(result["retrieved_documents"], start=1):
-                st.write(f"**Document {idx}:**")
-                st.write(doc[:500])  # Display the first 500 characters
-            st.write("### Blockchain Details:")
-            st.json(result["blockchain_details"])
-        elif result["status"] == "no_results":
-            st.warning("No relevant documents found for your query.")
-        elif result["status"] == "rejected":
-            st.error(result["message"])
-    else:
-        st.warning("Please enter a query to search.")
-
-# Debugging Section
-if st.checkbox("Show Debug Information"):
-    st.write(f"Total documents loaded: {len(system.retriever.documents)}")

app-news-content.py

@@ -1,105 +0,0 @@
-import streamlit as st
-import requests
-from transformers import T5Tokenizer, T5ForConditionalGeneration
-from collections import OrderedDict
-
-# Load Models
-@st.cache_resource
-def load_models():
-    tokenizer = T5Tokenizer.from_pretrained("t5-small")
-    model = T5ForConditionalGeneration.from_pretrained("t5-small")
-    return tokenizer, model
-
-t5_tokenizer, t5_model = load_models()
-
-# API Key for NewsAPI
-NEWS_API_KEY = "66db8e116ae24c49beef53e8b879600a"
-
-# Fetch news articles based on the user query
-@st.cache_data
-def fetch_news(user_query):
-    NEWS_API_URL = "https://newsapi.org/v2/everything"
-    params = {
-        'q': user_query,
-        'apiKey': NEWS_API_KEY,
-        'language': 'en',
-        'pageSize': 10,  # Fetch 10 articles
-        'sortBy': 'relevance',
-    }
-    response = requests.get(NEWS_API_URL, params=params)
-    if response.status_code == 200:
-        articles = response.json().get('articles', [])
-        return [
-            {
-                'title': article.get('title', 'No Title'),
-                'description': article.get('description', 'No Description')
-            }
-            for article in articles if article.get('description')
-        ]
-    return []
-
-# Summarize articles
-def summarize_articles(articles):
-    summaries = []
-    for article in articles:
-        input_text = f"summarize: Title: {article['title']}. Description: {article['description']}"
-        inputs = t5_tokenizer.encode(input_text, return_tensors="pt", max_length=512, truncation=True)
-        outputs = t5_model.generate(inputs, max_length=100, min_length=50, length_penalty=2.0, num_beams=4, early_stopping=True)
-        summary = t5_tokenizer.decode(outputs[0], skip_special_tokens=True)
-        summaries.append(remove_redundancy(summary))
-    return summaries
-
-# Remove redundancy in summaries
-def remove_redundancy(summary):
-    sentences = summary.split('. ')
-    seen = OrderedDict()
-    return '. '.join([seen.setdefault(s, s) for s in sentences if s not in seen])
-
-# Generate catchy content based on all 10 summaries
-def generate_catchy_content(summarized_content):
-    combined_prompt = f"""
-    Write a blog post based on these insights:
-    {', '.join(summarized_content)}
-    """
-    inputs = t5_tokenizer.encode(combined_prompt, return_tensors="pt", max_length=512, truncation=True)
-    outputs = t5_model.generate(inputs, max_length=300, length_penalty=2.0, num_beams=4, early_stopping=True)
-    return t5_tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-# Main App
-st.title("News Summarizer & Blog Generator")
-st.subheader("Enter a topic to fetch news, summarize and generate engaging content!")
-
-# Query Input
-user_query = st.text_input("Enter a query (e.g., 'AI trends', 'Climate change impact'):")
-
-if st.button("Fetch, Summarize and Generate"):
-    if user_query:
-        st.info(f"Fetching articles related to: {user_query}")
-        with st.spinner("Fetching news articles..."):
-            articles = fetch_news(user_query)
-            if articles:
-                st.success(f"Fetched {len(articles)} articles!")
-                
-                # Display only the first 4 articles
-                st.subheader("Fetched Articles")
-                for i, article in enumerate(articles[:4], 1):
-                    st.write(f"**Article {i}:** {article['title']}")
-                    st.write(f"*Description:* {article['description']}")
-
-                # Summarize All Articles
-                st.info("Summarizing articles...")
-                summaries = summarize_articles(articles)  # Summarize all 10 articles
-                st.subheader("Summarized Articles")
-                for i, summary in enumerate(summaries[:4], 1):  # Display summaries for first 4 articles
-                    st.write(f"**Summary {i}:** {summary}")
-
-                # Generate Blog Post
-                st.info("Generating blog post...")
-                generated_content = generate_catchy_content(summaries)  # Use all 10 summaries
-                st.subheader("Generated Blog Post")
-                st.write(generated_content)
-
-            else:
-                st.warning("No articles found. Try a different query.")
-    else:
-        st.error("Please enter a query to proceed!")

app.py

@@ -1,28 +1,143 @@
+from pathlib import Path
+
 import streamlit as st
-import importlib.util
+from googlesearch import search
+import pandas as pd
+import os
+from rag_sec.document_search_system import DocumentSearchSystem
+from chainguard.blockchain_logger import BlockchainLogger
+from PIL import Image
+
+# Blockchain Logger
+blockchain_logger = BlockchainLogger()
+
+# Directory for storing uploaded files
+UPLOAD_DIR = "uploaded_files"
+os.makedirs(UPLOAD_DIR, exist_ok=True)
+
+# Initialize DocumentSearchSystem
+@st.cache_resource
+def initialize_system():
+    """Initialize the DocumentSearchSystem and load documents."""
+    system = DocumentSearchSystem(
+        neo4j_uri="neo4j+s://0ca71b10.databases.neo4j.io",
+        neo4j_user="neo4j",
+        neo4j_password="HwGDOxyGS1-79nLeTiX5bx5ohoFSpvHCmTv8IRgt-lY"
+    )
+    system.retriever.load_documents()
+    return system
+
+# Initialize the system
+system = initialize_system()
+
+st.title("Memora: Secure File Upload and Search with Blockchain & Neo4j")
+st.subheader("Personalized news and global updates at your fingertips")
+# File Upload Section
+uploaded_files = st.file_uploader("Upload your files", accept_multiple_files=True, type=['jpg', 'jpeg', 'png', 'mp4', 'avi'])
+
+if uploaded_files:
+    for uploaded_file in uploaded_files:
+        # Save file locally
+        file_path = os.path.join(UPLOAD_DIR, uploaded_file.name)
+        with open(file_path, "wb") as f:
+            f.write(uploaded_file.getbuffer())
+        st.success(f"File saved locally: {file_path}")
+
+        # Display uploaded file details
+        if uploaded_file.type.startswith('image'):
+            image = Image.open(uploaded_file)
+            st.image(image, caption=uploaded_file.name, use_column_width=True)
+
+        # Metadata Input
+        album = st.text_input(f"Album for {uploaded_file.name}", "Default Album")
+        tags = st.text_input(f"Tags for {uploaded_file.name} (comma-separated)", "")
+
+        # Log Metadata and Transaction
+        if st.button(f"Log Metadata for {uploaded_file.name}"):
+            metadata = {"file_name": uploaded_file.name, "tags": tags.split(','), "album": album}
+            blockchain_details = blockchain_logger.log_data(metadata)
+            blockchain_hash = blockchain_details.get("block_hash", "N/A")
+
+            # Use Neo4jHandler from DocumentSearchSystem to log the transaction
+            system.neo4j_handler.log_relationships(uploaded_file.name, tags, blockchain_hash, [album])
+            st.write(f"Metadata logged successfully! Blockchain Details: {blockchain_details}")
+
+# Blockchain Integrity Validation
+if st.button("Validate Blockchain Integrity"):
+    is_valid = blockchain_logger.is_blockchain_valid()
+    st.write("Blockchain Integrity:", "Valid ✅" if is_valid else "Invalid ❌")
+
+# Document Search Section
+st.subheader("Search Documents")
+
+# Google Search: User-Specific News
+st.subheader("1. Latest News About You")
+user_name = st.text_input("Enter your name or handle to search for recent news", value="Talex Maxim")
+
+if st.button("Search News About Me"):
+    if user_name:
+        st.write(f"Searching Google for news about **{user_name}**...")
+        try:
+            results = list(search(user_name, num_results=5))
+            if results:
+                st.success(f"Top {len(results)} results for '{user_name}':")
+                user_news_data = {"URL": results}
+                df_user_news = pd.DataFrame(user_news_data)
+                st.dataframe(df_user_news)
+            else:
+                st.warning("No recent news found about you.")
+        except Exception as e:
+            st.error(f"An error occurred during the search: {str(e)}")
+    else:
+        st.warning("Please enter your name or handle to search.")
 
-# Define the paths to the other apps
-FIRST_APP_PATH = "app-memora.py"
-SECOND_APP_PATH = "app-news-content.py"
+# Google Search: Global News Categories
+st.subheader("2. Global News Insights")
+categories = ["Technology", "Sports", "Politics", "Entertainment", "Science"]
+news_results = {}
 
-def run_script(script_path):
-    """
-    Dynamically load and execute a Python script's main() function.
-    """
-    # Load the module from the script path
-    spec = importlib.util.spec_from_file_location("module.name", script_path)
-    module = importlib.util.module_from_spec(spec)
-    spec.loader.exec_module(module)
+if st.button("Fetch Global News"):
+    try:
+        for category in categories:
+            st.write(f"Fetching news for **{category}**...")
+            try:
+                category_results = list(search(f"latest {category} news", num_results=3))
+                news_results[category] = category_results
+            except Exception as e:
+                news_results[category] = [f"Error fetching news: {str(e)}"]
 
-def main():
-    st.sidebar.title("Navigation")
-    app_choice = st.sidebar.radio("Choose an App:", ["Memora", "Dynamic News Summarizer"])
+        # Display results
+        for category, articles in news_results.items():
+            st.write(f"### Top News in {category}:")
+            for idx, article in enumerate(articles, start=1):
+                st.write(f"{idx}. [Read here]({article})")
+    except Exception as e:
+        st.error(f"An error occurred while fetching global news: {str(e)}")
 
-    if app_choice == "Memora":
-        run_script(FIRST_APP_PATH)
-    elif app_choice == "Dynamic News Summarizer":
-        run_script(SECOND_APP_PATH)
+# Document Search
+st.subheader("3. Search Documents")
+query = st.text_input("Enter your query (e.g., 'sports news', 'machine learning')")
 
+if st.button("Search Documents"):
+    if query:
+        result = system.process_query(query)
+        if result["status"] == "success":
+            st.success(f"Query processed successfully!")
+            st.write("### Query Response:")
+            st.write(result["response"])
+            st.write("### Retrieved Documents:")
+            for idx, doc in enumerate(result["retrieved_documents"], start=1):
+                st.write(f"**Document {idx}:**")
+                st.write(doc[:500])  # Display the first 500 characters
+            st.write("### Blockchain Details:")
+            st.json(result["blockchain_details"])
+        elif result["status"] == "no_results":
+            st.warning("No relevant documents found for your query.")
+        elif result["status"] == "rejected":
+            st.error(result["message"])
+    else:
+        st.warning("Please enter a query to search.")
 
-if __name__ == "__main__":
-    main()
+# Debugging Section
+if st.checkbox("Show Debug Information"):
+    st.write(f"Total documents loaded: {len(system.retriever.documents)}")

devops_ai/deployment_script.py

@@ -1,60 +0,0 @@
-import os
-import json
-from openai import OpenAI
-
-def generate_helm_chart():
-    client = OpenAI(
-        base_url="https://api.studio.nebius.ai/v1/",
-        api_key="N_KEY"
-    )
-    prompt = """
-    pipeline {
-    agent any
-    stages {
-        stage('Build') {
-            steps {
-                echo 'Building...'
-                sh './gradlew build'
-            }
-        }
-        stage('Test') {
-            steps {
-                echo 'Testing...'
-                sh './gradlew test'
-            }
-        }
-        stage('Deploy') {
-            steps {
-                echo 'Deploying...'
-                sh './deploy.sh'
-            }
-        }
-    }
-}
-        """
-    response = client.chat.completions.create(
-        model="Qwen/Qwen2-VL-72B-Instruct",
-        messages=[
-            {
-                "role": "user",
-                "content": [
-                    {"type": "text",
-                     "text": f"Convert this Jenkinsfile into a GitHub Actions workflow:\n\n{prompt}"}
-                ],
-            }
-        ],
-        max_tokens=300,
-    )
-
-    # Extracting and prettifying the content
-    if response.choices:
-        helm_chart_raw = response.choices[0].message.content
-
-        # Prettifying the output
-        print("\n--- Helm Chart Output (Prettified) ---\n")
-        print(helm_chart_raw.strip())
-    else:
-        print("No response received from the API.")
-
-if __name__ == "__main__":
-    generate_helm_chart()

devops_ai/example-service_helm_chart.yaml

@@ -1,26 +0,0 @@
-Here's an example of how you can deploy an AWS Lambda function using CloudFormation. This Lambda function will be triggered when an object is created in a specified S3 bucket, and it will convert all uploaded files to PDF format:
-1. **Template Preparation:**
-   - Create a new directory on your local machine where you will store the template file and Lambda function code.
-   - Write a Python code for the Lambda function that converts files to PDF. Here's a simple example using the `python-pdf` library:
-     ```python
-     import boto3
-     import pdfkit
-     s3 = boto3.client('s3')
-
-     def convert_to_pdf(event, context):
-         # Extract file name and bucket name from the event
-         bucket_name = event['Records'][0]['s3']['bucket']['name']
-         file_name = event['Records'][0]['s3']['object']['key']
-
-         # Download the file from S3
-         response = s3.get_object(Bucket=bucket_name, Key=file_name)
-         original_file_data = response['Body'].read()
-
-         # Convert the file to PDF
-         pdf_file = pdfkit.from_string(original_file_data)
-         pdf_filename = file_name + '.pdf'
-
-         # Upload the PDF back to S3
-         upload_response = s3.put_object(
-             Body=pdf_file,
-             Bucket=

devops_ai/helm_chart_generator.py

@@ -1,45 +0,0 @@
-
-
-import cohere
-
-# Initialize Cohere client
-co = cohere.Client("nvnj")
-
-def generate_helm_chart(prompt):
-    prompt = f"Generate a Kubernetes Helm chart for a microservice named '{service_name}' in YAML format."
-    response = co.generate(
-        model="command",
-        prompt=prompt,
-        max_tokens=300,
-        temperature=0.7,
-    )
-    return response.generations[0].text.strip()
-
-# Example Usage
-service_name = "example-service"
-helm_chart = generate_helm_chart(service_name)
-print(helm_chart)
-
-
-def save_exact_output_to_file(output, file_name):
-    try:
-        # Save the exact string output to the file
-        with open(file_name, "w") as file:
-            file.write(output)
-        print(f"Output saved successfully to {file_name}")
-    except Exception as e:
-        print(f"Error saving output: {e}")
-
-
-# Example Usage
-if __name__ == "__main__":
-    service_name = "example-service"
-    try:
-        # Generate the Helm chart
-        helm_chart = generate_helm_chart(service_name)
-
-        # Save the output to a YAML file
-        file_name = f"{service_name}_helm_chart.yaml"
-        save_exact_output_to_file(helm_chart, file_name)
-    except Exception as e:
-        print(f"Error: {e}")

rag_sec/document_search_system.py

@@ -155,10 +155,7 @@ class DocumentSearchSystem:
         blockchain_details = self.data_transformer.secure_transform({"type": "response", "content": response})
 
         # Log relationships to Neo4j
-        try:
-            self.neo4j_handler.log_relationships(query, transformed_query, response, retrieved_docs)
-        except Exception as e:
-            print(e)
+        self.neo4j_handler.log_relationships(query, transformed_query, response, retrieved_docs)
 
         return {
             "status": "success",

rag_sec/requirements.txt

@@ -5,4 +5,3 @@ scikit-learn
 faiss-cpu
 pandas
 transformers
-torch

requirements.txt

@@ -6,8 +6,4 @@ scikit-learn
 faiss-cpu
 pandas
 transformers
-googlesearch-python
-torch
-requests
-pyspellchecker
-sentencepiece
+googlesearch-python